OpenEuler Linux上怎么测试Nvidia显卡安装情况

news2024/12/20 18:29:13

当安装好显卡驱动后怎么样知道驱动程序安装好了,这里以T400 + OpenEuler 正常情况下,我们只要看一下nvidia-smi 状态就可以确定他已经正常了
如图:

这里就已经确定是可以正常使用了,这里只是没有运行对应的程序,那接来下我们就写一个测试程序来测试一下:以下代码通过AI给出然后做了一些小改

这里做两个文件:

首先，让我们创建一个C文件，命名为`gpu_matrix_multiply.cu`：

#include <stdio.h>
#include <stdlib.h>
#include <cuda_runtime.h>

#define N 1024  // Matrix size (N x N)
#define BLOCK_SIZE 32

__global__ void matrixMultiply(float *A, float *B, float *C) {
    int row = blockIdx.y * blockDim.y + threadIdx.y;
    int col = blockIdx.x * blockDim.x + threadIdx.x;
    float sum = 0.0f;

    if (row < N && col < N) {
        for (int i = 0; i < N; i++) {
            sum += A[row * N + i] * B[i * N + col];
        }
        C[row * N + col] = sum;
    }
}

void initMatrix(float *matrix) {
    for (int i = 0; i < N * N; i++) {
        matrix[i] = rand() / (float)RAND_MAX;
    }
}

int main() {
    float *h_A, *h_B, *h_C;
    float *d_A, *d_B, *d_C;
    size_t size = N * N * sizeof(float);

    // Allocate host memory
    h_A = (float*)malloc(size);
    h_B = (float*)malloc(size);
    h_C = (float*)malloc(size);

    // Initialize host matrices
    initMatrix(h_A);
    initMatrix(h_B);

    // Allocate device memory
    cudaMalloc(&d_A, size);
    cudaMalloc(&d_B, size);
    cudaMalloc(&d_C, size);

    // Copy host memory to device
    cudaMemcpy(d_A, h_A, size, cudaMemcpyHostToDevice);
    cudaMemcpy(d_B, h_B, size, cudaMemcpyHostToDevice);

    // Define grid and block dimensions
    dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE);
    dim3 dimGrid((N + dimBlock.x - 1) / dimBlock.x, (N + dimBlock.y - 1) / dimBlock.y);

    // Create CUDA events for timing
    cudaEvent_t start, stop;
    cudaEventCreate(&start);
    cudaEventCreate(&stop);

    // Record start event
    cudaEventRecord(start);

    // Launch kernel
    matrixMultiply<<<dimGrid, dimBlock>>>(d_A, d_B, d_C);

    // Record stop event
    cudaEventRecord(stop);
    cudaEventSynchronize(stop);

    // Calculate elapsed time
    float milliseconds = 0;
    cudaEventElapsedTime(&milliseconds, start, stop);

    printf("Matrix multiplication took %f ms\n", milliseconds);

    // Copy result back to host
    cudaMemcpy(h_C, d_C, size, cudaMemcpyDeviceToHost);

    // Clean up
    free(h_A); free(h_B); free(h_C);
    cudaFree(d_A); cudaFree(d_B); cudaFree(d_C);
    cudaEventDestroy(start); cudaEventDestroy(stop);

    return 0;
}

然后能用批处理就批处理,再来创建一个Shell脚本来编译和运行这个程序。将以下内容保存为`compile_and_run.sh`：

#!/bin/bash

# Compile the CUDA program
nvcc -o gpu_matrix_multiply gpu_matrix_multiply.cu

# Check if compilation was successful
if [ $? -eq 0 ]; then
    echo "Compilation successful. Running the program..."
    # Run the program
    ./gpu_matrix_multiply
else
    echo "Compilation failed."
fi

然后就是跑起来: